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http://dx.doi.org/10.1016/j.advnut.2024.100334 | DOI Listing |
Best Pract Res Clin Anaesthesiol
March 2024
Department of Surgery, Universitat de València, 46010 València, Spain; Ivano-Frankivsk National Medical University, 76018 Ivano-Frankivsk, Ukraine.
Health care workers are at risk of infection from aerosolization of respiratory secretions, droplet and contact spread. This has gained great importance after the COVID19 pandemic. Intra-operative aerosol-generating procedures are arguably unavoidable in the routine provision of thoracic anesthesia.
View Article and Find Full Text PDFBest Pract Res Clin Anaesthesiol
September 2024
Division of Maternal-Fetal Medicine, Brigham and Women's Hospital, Harvard Medical School, 75 Francis St, L1, Boston, MA, 02115, USA. Electronic address:
Preeclampsia is a life-threatening complication that develops in 2-8% of pregnancies. It is characterized by elevated blood pressure after 20 weeks of gestation and may progress to multiorgan dysfunction, leading to severe maternal and fetal morbidity and mortality. The only definitive treatment is delivery, and efforts are focused on early risk prediction, surveillance, and severity mitigation.
View Article and Find Full Text PDFBest Pract Res Clin Anaesthesiol
September 2024
K. Bicetre School of Medicine, Paris-Saclay University, Département d'Anesthésie, Hôpital Antoine Béclère - APHP.Université Paris-Saclay, 157 rue de la porte de Trivaux, 92140, CLAMART, France. Electronic address:
This article offers a comprehensive clinical update on best practices for neuraxial and general anesthesia in cesarean delivery, the most frequently performed major surgical procedure globally. Current evidence-based strategies to address common anesthetic challenges, such as maternal hypotension and intraoperative breakthrough pain, are discussed in detail. Practical approaches for optimizing maternal hemodynamic stability, including the use of vasopressors, fluid management and maternal positioning, are reviewed.
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January 2025
Department of Clinical Pharmacy, Zhejiang Provincial Key Laboratory for Drug Evaluation and Clinical Research, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China.
Efforts have been made to leverage technology to accurately identify tumor characteristics and predict how each cancer patient may respond to medications. This involves collecting data from various sources such as genomic data, histological information, functional drug profiling, and drug metabolism using techniques like polymerase chain reaction, sanger sequencing, next-generation sequencing, fluorescence in situ hybridization, immunohistochemistry staining, patient-derived tumor xenograft models, patient-derived organoid models, and therapeutic drug monitoring. The utilization of diverse detection technologies in clinical practice has made "individualized treatment" possible, but the desired level of accuracy has not been fully attained yet.
View Article and Find Full Text PDFEur Phys J C Part Fields
January 2025
MaLGa-DIBRIS, University of Genoa, Genoa, Italy.
In this work, we address the question of how to enhance signal-agnostic searches by leveraging multiple testing strategies. Specifically, we consider hypothesis tests relying on machine learning, where model selection can introduce a bias towards specific families of new physics signals. Focusing on the New Physics Learning Machine, a methodology to perform a signal-agnostic likelihood-ratio test, we explore a number of approaches to multiple testing, such as combining -values and aggregating test statistics.
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